{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Stellargraph example: Graph Convolutional Network (GCN) on the CORA citation dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Import NetworkX and stellar:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import networkx as nx\n",
    "import pandas as pd\n",
    "import os\n",
    "\n",
    "import stellargraph as sg\n",
    "from stellargraph.mapper import FullBatchNodeGenerator\n",
    "from stellargraph.layer import GCN\n",
    "\n",
    "from tensorflow.keras import layers, optimizers, losses, metrics, Model\n",
    "from sklearn import preprocessing, feature_extraction, model_selection\n",
    "from stellargraph import datasets\n",
    "from IPython.display import display, HTML"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Loading the CORA network"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "The Cora dataset consists of 2708 scientific publications classified into one of seven classes. The citation network consists of 5429 links. Each publication in the dataset is described by a 0/1-valued word vector indicating the absence/presence of the corresponding word from the dictionary. The dictionary consists of 1433 unique words."
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dataset = datasets.Cora()\n",
    "display(HTML(dataset.description))\n",
    "dataset.download()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load the graph from edgelist (in the order `cited-paper` <- `citing-paper`)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "edgelist = pd.read_csv(\n",
    "    os.path.join(dataset.data_directory, \"cora.cites\"),\n",
    "    sep=\"\\t\",\n",
    "    header=None,\n",
    "    names=[\"target\", \"source\"],\n",
    ")\n",
    "edgelist[\"label\"] = \"cites\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "Gnx = nx.from_pandas_edgelist(edgelist, edge_attr=\"label\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "nx.set_node_attributes(Gnx, \"paper\", \"label\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load the features and subject for the nodes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "feature_names = [\"w_{}\".format(ii) for ii in range(1433)]\n",
    "column_names = feature_names + [\"subject\"]\n",
    "node_data = pd.read_csv(\n",
    "    os.path.join(dataset.data_directory, \"cora.content\"),\n",
    "    sep=\"\\t\",\n",
    "    header=None,\n",
    "    names=column_names,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We aim to train a graph-ML model that will predict the \"subject\" attribute on the nodes. These subjects are one of 7 categories:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Case_Based',\n",
       " 'Genetic_Algorithms',\n",
       " 'Neural_Networks',\n",
       " 'Probabilistic_Methods',\n",
       " 'Reinforcement_Learning',\n",
       " 'Rule_Learning',\n",
       " 'Theory'}"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "set(node_data[\"subject\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Splitting the data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For machine learning we want to take a subset of the nodes for training, and use the rest for validation and testing. We'll use scikit-learn again to do this.\n",
    "\n",
    "Here we're taking 140 node labels for training, 500 for validation, and the rest for testing."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data, test_data = model_selection.train_test_split(\n",
    "    node_data, train_size=140, test_size=None, stratify=node_data[\"subject\"]\n",
    ")\n",
    "val_data, test_data = model_selection.train_test_split(\n",
    "    test_data, train_size=500, test_size=None, stratify=test_data[\"subject\"]\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note using stratified sampling gives the following counts:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Counter({'Case_Based': 16,\n",
       "         'Rule_Learning': 9,\n",
       "         'Probabilistic_Methods': 22,\n",
       "         'Theory': 18,\n",
       "         'Neural_Networks': 42,\n",
       "         'Genetic_Algorithms': 22,\n",
       "         'Reinforcement_Learning': 11})"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from collections import Counter\n",
    "\n",
    "Counter(train_data[\"subject\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The training set has class imbalance that might need to be compensated, e.g., via using a weighted cross-entropy loss in model training, with class weights inversely proportional to class support. However, we will ignore the class imbalance in this example, for simplicity."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Converting to numeric arrays"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For our categorical target, we will use one-hot vectors that will be fed into a soft-max Keras layer during training. To do this conversion ..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "target_encoding = feature_extraction.DictVectorizer(sparse=False)\n",
    "\n",
    "train_targets = target_encoding.fit_transform(train_data[[\"subject\"]].to_dict(\"records\"))\n",
    "val_targets = target_encoding.transform(val_data[[\"subject\"]].to_dict(\"records\"))\n",
    "test_targets = target_encoding.transform(test_data[[\"subject\"]].to_dict(\"records\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We now do the same for the node attributes we want to use to predict the subject. These are the feature vectors that the Keras model will use as input. The CORA dataset contains attributes 'w_x' that correspond to words found in that publication. If a word occurs more than once in a publication the relevant attribute will be set to one, otherwise it will be zero."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "node_features = node_data[feature_names]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Creating the GCN model in Keras"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now create a StellarGraph object from the NetworkX graph and the node features and targets. It is StellarGraph objects that we use in this library to perform machine learning tasks on."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "G = sg.StellarGraph(Gnx, node_features=node_features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "NetworkXStellarGraph: Undirected multigraph\n",
      " Nodes: 2708, Edges: 5278\n",
      "\n",
      " Node types:\n",
      "  paper: [2708]\n",
      "    Edge types: paper-cites->paper\n",
      "\n",
      " Edge types:\n",
      "    paper-cites->paper: [5278]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(G.info())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To feed data from the graph to the Keras model we need a generator. Since GCN is a full-batch model, we use the `FullBatchNodeGenerator` class to feed node features and the normalized graph Laplacian matrix to the model.\n",
    "\n",
    "Specifying the `method='gcn'` argument to the `FullBatchNodeGenerator` will pre-process the adjacency matrix and supply the normalized graph Laplacian matrix to the model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using GCN (local pooling) filters...\n"
     ]
    }
   ],
   "source": [
    "generator = FullBatchNodeGenerator(G, method=\"gcn\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For training we map only the training nodes returned from our splitter and the target values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_gen = generator.flow(train_data.index, train_targets)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can specify our machine learning model, we need a few more parameters for this:\n",
    "\n",
    " * the `layer_sizes` is a list of hidden feature sizes of each layer in the model. In this example we use two GCN layers with 16-dimensional hidden node features at each layer.\n",
    " * `activations` is a list of activations applied to each layer's output\n",
    " * `dropout=0.5` specifies a 50% dropout at each layer. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We create a GCN model as follows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "gcn = GCN(\n",
    "    layer_sizes=[16, 16], activations=[\"relu\", \"relu\"], generator=generator, dropout=0.5\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To create a Keras model we now expose the input and output tensors of the GCN model for node prediction, via the `GCN.build` method:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_inp, x_out = gcn.build()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally we add a Keras `Dense` layer to use the node embeddings to predict the 7 categories."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions = layers.Dense(units=train_targets.shape[1], activation=\"softmax\")(x_out)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Training the model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's create the actual Keras model with the input tensors `x_inp` and output tensors being the predictions `predictions` from the final dense layer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = Model(inputs=x_inp, outputs=predictions)\n",
    "model.compile(\n",
    "    optimizer=optimizers.Adam(lr=0.01),\n",
    "    loss=losses.categorical_crossentropy,\n",
    "    metrics=[\"acc\"],\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Train the model, keeping track of its loss and accuracy on the training set, and its generalisation performance on the validation set (we need to create another generator over the validation data for this)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "val_gen = generator.flow(val_data.index, val_targets)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create callbacks for early stopping (if validation accuracy stops improving) and best model checkpoint saving:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras.callbacks import EarlyStopping, ModelCheckpoint\n",
    "\n",
    "if not os.path.isdir(\"logs\"):\n",
    "    os.makedirs(\"logs\")\n",
    "es_callback = EarlyStopping(\n",
    "    monitor=\"val_acc\", patience=50\n",
    ")  # patience is the number of epochs to wait before early stopping in case of no further improvement\n",
    "mc_callback = ModelCheckpoint(\n",
    "    \"logs/best_model.h5\", monitor=\"val_acc\", save_best_only=True, save_weights_only=True\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Train the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/40\n",
      "1/1 - 0s - loss: 1.9457 - acc: 0.1643 - val_loss: 1.9038 - val_acc: 0.3740\n",
      "Epoch 2/40\n",
      "1/1 - 0s - loss: 1.8987 - acc: 0.4000 - val_loss: 1.8558 - val_acc: 0.4340\n",
      "Epoch 3/40\n",
      "1/1 - 0s - loss: 1.8269 - acc: 0.4500 - val_loss: 1.7961 - val_acc: 0.4340\n",
      "Epoch 4/40\n",
      "1/1 - 0s - loss: 1.7415 - acc: 0.4643 - val_loss: 1.7309 - val_acc: 0.4280\n",
      "Epoch 5/40\n",
      "1/1 - 0s - loss: 1.6377 - acc: 0.4643 - val_loss: 1.6599 - val_acc: 0.4260\n",
      "Epoch 6/40\n",
      "1/1 - 0s - loss: 1.5815 - acc: 0.4500 - val_loss: 1.5829 - val_acc: 0.4380\n",
      "Epoch 7/40\n",
      "1/1 - 0s - loss: 1.4642 - acc: 0.4857 - val_loss: 1.5071 - val_acc: 0.4680\n",
      "Epoch 8/40\n",
      "1/1 - 0s - loss: 1.3575 - acc: 0.4929 - val_loss: 1.4341 - val_acc: 0.4920\n",
      "Epoch 9/40\n",
      "1/1 - 0s - loss: 1.2922 - acc: 0.5000 - val_loss: 1.3610 - val_acc: 0.5160\n",
      "Epoch 10/40\n",
      "1/1 - 0s - loss: 1.1731 - acc: 0.4929 - val_loss: 1.2949 - val_acc: 0.5360\n",
      "Epoch 11/40\n",
      "1/1 - 0s - loss: 1.1207 - acc: 0.5500 - val_loss: 1.2370 - val_acc: 0.5480\n",
      "Epoch 12/40\n",
      "1/1 - 0s - loss: 1.0147 - acc: 0.5786 - val_loss: 1.1842 - val_acc: 0.5560\n",
      "Epoch 13/40\n",
      "1/1 - 0s - loss: 0.9561 - acc: 0.5929 - val_loss: 1.1344 - val_acc: 0.5700\n",
      "Epoch 14/40\n",
      "1/1 - 0s - loss: 0.9568 - acc: 0.5571 - val_loss: 1.0847 - val_acc: 0.6200\n",
      "Epoch 15/40\n",
      "1/1 - 0s - loss: 0.8202 - acc: 0.6643 - val_loss: 1.0339 - val_acc: 0.6480\n",
      "Epoch 16/40\n",
      "1/1 - 0s - loss: 0.7821 - acc: 0.7143 - val_loss: 0.9839 - val_acc: 0.6860\n",
      "Epoch 17/40\n",
      "1/1 - 0s - loss: 0.7269 - acc: 0.7857 - val_loss: 0.9373 - val_acc: 0.7160\n",
      "Epoch 18/40\n",
      "1/1 - 0s - loss: 0.5948 - acc: 0.8214 - val_loss: 0.8948 - val_acc: 0.7380\n",
      "Epoch 19/40\n",
      "1/1 - 0s - loss: 0.5854 - acc: 0.8500 - val_loss: 0.8553 - val_acc: 0.7560\n",
      "Epoch 20/40\n",
      "1/1 - 0s - loss: 0.5307 - acc: 0.8571 - val_loss: 0.8164 - val_acc: 0.7640\n",
      "Epoch 21/40\n",
      "1/1 - 0s - loss: 0.4895 - acc: 0.8929 - val_loss: 0.7818 - val_acc: 0.7760\n",
      "Epoch 22/40\n",
      "1/1 - 0s - loss: 0.3814 - acc: 0.9214 - val_loss: 0.7523 - val_acc: 0.7800\n",
      "Epoch 23/40\n",
      "1/1 - 0s - loss: 0.3885 - acc: 0.9000 - val_loss: 0.7267 - val_acc: 0.7980\n",
      "Epoch 24/40\n",
      "1/1 - 0s - loss: 0.3595 - acc: 0.9357 - val_loss: 0.7058 - val_acc: 0.8060\n",
      "Epoch 25/40\n",
      "1/1 - 0s - loss: 0.3202 - acc: 0.9643 - val_loss: 0.6890 - val_acc: 0.8040\n",
      "Epoch 26/40\n",
      "1/1 - 0s - loss: 0.2897 - acc: 0.9500 - val_loss: 0.6772 - val_acc: 0.7960\n",
      "Epoch 27/40\n",
      "1/1 - 0s - loss: 0.2539 - acc: 0.9500 - val_loss: 0.6717 - val_acc: 0.7940\n",
      "Epoch 28/40\n",
      "1/1 - 0s - loss: 0.2849 - acc: 0.9286 - val_loss: 0.6654 - val_acc: 0.7960\n",
      "Epoch 29/40\n",
      "1/1 - 0s - loss: 0.2135 - acc: 0.9571 - val_loss: 0.6585 - val_acc: 0.7940\n",
      "Epoch 30/40\n",
      "1/1 - 0s - loss: 0.1719 - acc: 0.9643 - val_loss: 0.6555 - val_acc: 0.7980\n",
      "Epoch 31/40\n",
      "1/1 - 0s - loss: 0.1753 - acc: 0.9571 - val_loss: 0.6576 - val_acc: 0.8020\n",
      "Epoch 32/40\n",
      "1/1 - 0s - loss: 0.1865 - acc: 0.9571 - val_loss: 0.6626 - val_acc: 0.8040\n",
      "Epoch 33/40\n",
      "1/1 - 0s - loss: 0.1510 - acc: 0.9714 - val_loss: 0.6687 - val_acc: 0.8020\n",
      "Epoch 34/40\n",
      "1/1 - 0s - loss: 0.1567 - acc: 0.9429 - val_loss: 0.6737 - val_acc: 0.7980\n",
      "Epoch 35/40\n",
      "1/1 - 0s - loss: 0.1276 - acc: 0.9429 - val_loss: 0.6767 - val_acc: 0.8060\n",
      "Epoch 36/40\n",
      "1/1 - 0s - loss: 0.1417 - acc: 0.9571 - val_loss: 0.6814 - val_acc: 0.8020\n",
      "Epoch 37/40\n",
      "1/1 - 0s - loss: 0.0813 - acc: 0.9857 - val_loss: 0.6876 - val_acc: 0.7960\n",
      "Epoch 38/40\n",
      "1/1 - 0s - loss: 0.1411 - acc: 0.9786 - val_loss: 0.6943 - val_acc: 0.7940\n",
      "Epoch 39/40\n",
      "1/1 - 0s - loss: 0.1180 - acc: 0.9714 - val_loss: 0.6980 - val_acc: 0.7980\n",
      "Epoch 40/40\n",
      "1/1 - 0s - loss: 0.1107 - acc: 0.9571 - val_loss: 0.6966 - val_acc: 0.8080\n"
     ]
    }
   ],
   "source": [
    "history = model.fit_generator(\n",
    "    train_gen,\n",
    "    epochs=40,\n",
    "    validation_data=val_gen,\n",
    "    verbose=2,\n",
    "    shuffle=False,  # this should be False, since shuffling data means shuffling the whole graph\n",
    "    callbacks=[es_callback, mc_callback],\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot the training history:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "def remove_prefix(text, prefix):\n",
    "    return text[text.startswith(prefix) and len(prefix):]\n",
    "\n",
    "def plot_history(history):\n",
    "    metrics = sorted(set([remove_prefix(m, \"val_\") for m in list(history.history.keys())]))\n",
    "    for m in metrics:\n",
    "        # summarize history for metric m\n",
    "        plt.plot(history.history[m])\n",
    "        plt.plot(history.history['val_' + m])\n",
    "        plt.title(m)\n",
    "        plt.ylabel(m)\n",
    "        plt.xlabel('epoch')\n",
    "        plt.legend(['train', 'validation'], loc='best')\n",
    "        plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_history(history)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Reload the saved weights of the best model found during the training (according to validation accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.load_weights(\"logs/best_model.h5\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Evaluate the best model on the test set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_gen = generator.flow(test_data.index, test_targets)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Test Set Metrics:\n",
      "\tloss: 0.8057\n",
      "\tacc: 0.7829\n"
     ]
    }
   ],
   "source": [
    "test_metrics = model.evaluate_generator(test_gen)\n",
    "print(\"\\nTest Set Metrics:\")\n",
    "for name, val in zip(model.metrics_names, test_metrics):\n",
    "    print(\"\\t{}: {:0.4f}\".format(name, val))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Making predictions with the model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's get the predictions for all nodes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_nodes = node_data.index\n",
    "all_gen = generator.flow(all_nodes)\n",
    "all_predictions = model.predict_generator(all_gen)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "These predictions will be the output of the softmax layer, so to get final categories we'll use the `inverse_transform` method of our target attribute specifcation to turn these values back to the original categories."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that for full-batch methods the batch size is 1 and the predictions have shape $(1, N_{nodes}, N_{classes})$ so we we remove the batch dimension to obtain predictions of shape $(N_{nodes}, N_{classes})$ using the NumPy `squeeze` method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "node_predictions = target_encoding.inverse_transform(all_predictions.squeeze())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's have a look at a few predictions after training the model:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Predicted</th>\n",
       "      <th>True</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>31336</th>\n",
       "      <td>subject=Neural_Networks</td>\n",
       "      <td>Neural_Networks</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1061127</th>\n",
       "      <td>subject=Rule_Learning</td>\n",
       "      <td>Rule_Learning</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1106406</th>\n",
       "      <td>subject=Reinforcement_Learning</td>\n",
       "      <td>Reinforcement_Learning</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13195</th>\n",
       "      <td>subject=Reinforcement_Learning</td>\n",
       "      <td>Reinforcement_Learning</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37879</th>\n",
       "      <td>subject=Probabilistic_Methods</td>\n",
       "      <td>Probabilistic_Methods</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1126012</th>\n",
       "      <td>subject=Probabilistic_Methods</td>\n",
       "      <td>Probabilistic_Methods</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1107140</th>\n",
       "      <td>subject=Reinforcement_Learning</td>\n",
       "      <td>Theory</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1102850</th>\n",
       "      <td>subject=Neural_Networks</td>\n",
       "      <td>Neural_Networks</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31349</th>\n",
       "      <td>subject=Neural_Networks</td>\n",
       "      <td>Neural_Networks</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1106418</th>\n",
       "      <td>subject=Theory</td>\n",
       "      <td>Theory</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1123188</th>\n",
       "      <td>subject=Neural_Networks</td>\n",
       "      <td>Neural_Networks</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1128990</th>\n",
       "      <td>subject=Genetic_Algorithms</td>\n",
       "      <td>Genetic_Algorithms</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109323</th>\n",
       "      <td>subject=Probabilistic_Methods</td>\n",
       "      <td>Probabilistic_Methods</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>217139</th>\n",
       "      <td>subject=Case_Based</td>\n",
       "      <td>Case_Based</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31353</th>\n",
       "      <td>subject=Neural_Networks</td>\n",
       "      <td>Neural_Networks</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32083</th>\n",
       "      <td>subject=Neural_Networks</td>\n",
       "      <td>Neural_Networks</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1126029</th>\n",
       "      <td>subject=Reinforcement_Learning</td>\n",
       "      <td>Reinforcement_Learning</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1118017</th>\n",
       "      <td>subject=Neural_Networks</td>\n",
       "      <td>Neural_Networks</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49482</th>\n",
       "      <td>subject=Neural_Networks</td>\n",
       "      <td>Neural_Networks</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>753265</th>\n",
       "      <td>subject=Neural_Networks</td>\n",
       "      <td>Neural_Networks</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                              Predicted                    True\n",
       "31336           subject=Neural_Networks         Neural_Networks\n",
       "1061127           subject=Rule_Learning           Rule_Learning\n",
       "1106406  subject=Reinforcement_Learning  Reinforcement_Learning\n",
       "13195    subject=Reinforcement_Learning  Reinforcement_Learning\n",
       "37879     subject=Probabilistic_Methods   Probabilistic_Methods\n",
       "1126012   subject=Probabilistic_Methods   Probabilistic_Methods\n",
       "1107140  subject=Reinforcement_Learning                  Theory\n",
       "1102850         subject=Neural_Networks         Neural_Networks\n",
       "31349           subject=Neural_Networks         Neural_Networks\n",
       "1106418                  subject=Theory                  Theory\n",
       "1123188         subject=Neural_Networks         Neural_Networks\n",
       "1128990      subject=Genetic_Algorithms      Genetic_Algorithms\n",
       "109323    subject=Probabilistic_Methods   Probabilistic_Methods\n",
       "217139               subject=Case_Based              Case_Based\n",
       "31353           subject=Neural_Networks         Neural_Networks\n",
       "32083           subject=Neural_Networks         Neural_Networks\n",
       "1126029  subject=Reinforcement_Learning  Reinforcement_Learning\n",
       "1118017         subject=Neural_Networks         Neural_Networks\n",
       "49482           subject=Neural_Networks         Neural_Networks\n",
       "753265          subject=Neural_Networks         Neural_Networks"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results = pd.DataFrame(node_predictions, index=all_nodes).idxmax(axis=1)\n",
    "df = pd.DataFrame({\"Predicted\": results, \"True\": node_data[\"subject\"]})\n",
    "df.head(20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Node embeddings\n",
    "Evaluate node embeddings as activations of the output of the last graph convolution layer in the GCN layer stack and visualise them, coloring nodes by their true subject label. We expect to see nice clusters of papers in the node embedding space, with papers of the same subject belonging to the same cluster.\n",
    "\n",
    "To calculate the node embeddings rather than the class predictions, we create a new model with the same inputs as we used previously `x_inp` but now the output is the embeddings `x_out` rather than the predicted class. Additionally note that the weights trained previously are kept in the new model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "embedding_model = Model(inputs=x_inp, outputs=x_out)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1, 2708, 16)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "emb = embedding_model.predict_generator(all_gen)\n",
    "emb.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Project the embeddings to 2d using either TSNE or PCA transform, and visualise, coloring nodes by their true subject label"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.decomposition import PCA\n",
    "from sklearn.manifold import TSNE\n",
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that the embeddings from the GCN model have a batch dimension of 1 so we `squeeze` this to get a matrix of $N_{nodes} \\times N_{emb}$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = emb.squeeze(0)\n",
    "y = np.argmax(\n",
    "    target_encoding.transform(node_data[[\"subject\"]].to_dict(\"records\")), axis=1\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "if X.shape[1] > 2:\n",
    "    transform = TSNE  # PCA\n",
    "\n",
    "    trans = transform(n_components=2)\n",
    "    emb_transformed = pd.DataFrame(trans.fit_transform(X), index=list(G.nodes()))\n",
    "    emb_transformed[\"label\"] = y\n",
    "else:\n",
    "    emb_transformed = pd.DataFrame(X, index=list(G.nodes()))\n",
    "    emb_transformed = emb_transformed.rename(columns={\"0\": 0, \"1\": 1})\n",
    "    emb_transformed[\"label\"] = y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "alpha = 0.7\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(7, 7))\n",
    "ax.scatter(\n",
    "    emb_transformed[0],\n",
    "    emb_transformed[1],\n",
    "    c=emb_transformed[\"label\"].astype(\"category\"),\n",
    "    cmap=\"jet\",\n",
    "    alpha=alpha,\n",
    ")\n",
    "ax.set(aspect=\"equal\", xlabel=\"$X_1$\", ylabel=\"$X_2$\")\n",
    "plt.title(\n",
    "    \"{} visualization of GCN embeddings for cora dataset\".format(transform.__name__)\n",
    ")\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
